论文标题
HHH：基于知识图和分层双向关注的在线医疗聊天机器人系统
HHH: An Online Medical Chatbot System based on Knowledge Graph and Hierarchical Bi-Directional Attention
论文作者
论文摘要
本文提出了一个聊天机器人框架，该聊天机器人框架采用了由知识图和文本相似性模型组成的混合模型。基于此聊天机器人框架，我们构建了HHH，这是一个在线问答（QA）医疗助手系统，用于回答复杂的医疗问题。 HHH维护了一个从互联网收集的医疗数据构建的知识图。 HHH还实现了一种新颖的文本表示和相似性深度学习模型，分层Bilstm注意模型（HBAM），以从大型质量检查数据集中找到最相似的问题。我们将HBAM与其他最先进的语言模型进行比较，例如来自变形金刚（BERT）和Manhattan LSTM模型（MALSTM）的双向编码器表示。我们使用医疗区域中的Quora重复问题数据集的子集训练和测试模型。实验结果表明，我们的模型能够达到比这些现有方法的卓越性能。
This paper proposes a chatbot framework that adopts a hybrid model which consists of a knowledge graph and a text similarity model. Based on this chatbot framework, we build HHH, an online question-and-answer (QA) Healthcare Helper system for answering complex medical questions. HHH maintains a knowledge graph constructed from medical data collected from the Internet. HHH also implements a novel text representation and similarity deep learning model, Hierarchical BiLSTM Attention Model (HBAM), to find the most similar question from a large QA dataset. We compare HBAM with other state-of-the-art language models such as bidirectional encoder representation from transformers (BERT) and Manhattan LSTM Model (MaLSTM). We train and test the models with a subset of the Quora duplicate questions dataset in the medical area. The experimental results show that our model is able to achieve a superior performance than these existing methods.
